Many properties of organisms show great robustness against genetic and environmental perturbations. The terms canalization and developmental stability were originally proposed to describe the ability of an organism to resist perturbations and to produce a predictable target phenotype regardless of random developmental noise. However, the extent to which canalization and developmental stability are controlled by the same set of genes and share underlying regulatory mechanisms is largely unresolved.Drosophila subobscura. For the analysis of overall size, developmental stability was positively correlated with levels of heterozygosity and development at the optimal temperature. For analyses of shape, the overall comparisons by matrix correlations indicate that inter- and intraindividual variation levels were poorly correlated, a result also supported when comparing the vectors describing patterns of variation of landmark position. The lack of similarity was basically due to the discrepancy between the genetic and environmental components of the interindividual variation. Finally, the analyses have also underscored the presence of genetic variation for directional asymmetry.We have analyzed the effects of clinal genetic variation (inversion polymorphism) on wing asymmetry by applying the methods of geometric morphometrics in the context of quantitative genetics using isochromosomal lines of Drosophila populations is loosely related to individual fitness.The results strongly support the hypothesis that environmental canalization and developmental stability share underlying regulatory mechanisms, but environmental and genetic canalization are not functionally the same. A likely explanation for this lack of association is that natural wing shape variation in  Phenotypic robustness refers to the invariance of the specified target phenotype given the genetic makeup and environmental conditions. Whereas the presence of naturally occurring phenotypic variation is at the core of evolutionary biology, developmental geneticists have traditionally considered it as a nuisance. Instead, they have relied on the study of single or multiple mutant combinations to reveal the generation of phenotypic patterns (e.g. ). A resuThree major processes are involved in the control of phenotypic variability : canali) and special (micro) environmental effects : the first refer to influential factors (e.g. temperature) that are shared by groups of individuals, whereas the latter are residual deviations from the phenotype that would be specified on the basis of genotype and general environmental effects. Such deviations are unique to individuals and are largely unpredictable. The variance associated with special environmental effects can be estimated when experiments are performed on completely inbred lines . In bilaterally symmetrical organisms it is also feasible to estimate the two sources that contribute to those special environmental effects: among-individual  and within-individual variance . If the only real cause of asymmetry is variation due to stochasticity in development, then FA can be taken as an estimated of . Therefore, FA is only one source of the phenotypic variation within environments , contrarily to the arguments in Nijhout and Davidovitz  have a mean of zero, the interaction term is a measure of fluctuating asymmetry  provided that there is no genetic variation for DA [To first partition the total phenotypic variation into interindividual, intraindividual and measurement error components, we used the conventional mixed model, two-way ANOVA  for the study of left-right asymmetries . In thisn for DA , and the; there is no genetic variance within crosses). An estimated of the among-fly special environmental effects variance (i.e. ) is therefore obtained by subtracting the individual × side interaction effect (which includes  plus measurement error) as the appropriate error term. However, when genetic variation for DA is present the unbiased within-fly special environmental effects variance (i.e. FA) is estimated after partitioning the individual × side interaction effect into its causal components [We now digress slightly to point out some inconsistencies in the literature on what is the appropriate error term to test for the "interindividual" effect in the mixed model, two-way ANOVA  outbred crosses the karyotype covariance components were calculated as  , the cross covariance components as , and the among-fly special environmental effects covariance components as .The SSCPIS matrix into their causal components [Similarly, genetic effects for DA can be investigated after partitioning the SSCPmponents . for i ≠ j,  for i = j; superscript 'denotes transposition) in an n-dimensional space.Within each sex and temperature, principal component analyses  of the Vx- and y-coordinates  since covariance matrices are symmetrical, and statistical significance was assessed using permutation tests designed to maintain the associations between pairs of  columns ,15); othA second test examined the congruence of the landmark displacements corresponding to each emergent PC for the different effects within groups. Because the PCs correspond to directions in the multivariate shape space, correlations can be obtained by angular comparisons of component vectors. Statistical significance of these correlations was then assessed by comparing those observed values to a null distribution of absolute angles between 100,000 pairs of 22-dimensional random vectors . The 0.1) [) against mean centroid size .The occurrence of antisymmetry  for cen) . The indP-values > 0.10) and, therefore, no size corrections were necessary.Scatter plots of left-right differences for each landmark after Procrustes superimposition were visually checked to see whether or not there was evidence for clustering of these vectors that would have argue for the occurrence of AS ,15. No iThe computer programs used for statistical data analyses were MATLAB (V.6. ) togetheMS conceived the study, carried out extraction of O chromosomes, experimental crosses, egg collections, statistical analyses, and drafted the final manuscript. PFI carried out extraction of O chromosomes, experimental crosses, egg collections, wing measurements, and preliminary statistical analyses and drafts of results. WC read all salivary gland squashes for gene arrangement identification and mounted the wings on microscope slides. All authors read and approved the final manuscript.


Wolbachia infection in D. bifasciata in Hokkaido island of Japan, in contrast to the presence of infection on the proximal island of Honshu, was associated with failure of the infection to function properly on the Hokkaido genetic background.Selfish genetic elements that distort the sex ratio are found widely. Notwithstanding the number of records of sex ratio distorters, their incidence is poorly understood. Two factors can prevent a sex ratio distorter from invading: inability of the sex ratio distorter to function (failure of mechanism or transmission), and lack of drive if they do function (inappropriate ecology for invasion). There has been no test to date on factors causing variation in the incidence of sex ratio distorting cytoplasmic bacteria. We therefore examined whether absence of the male-killing D. bifasciata may occur at different densities in Hokkaido and Honshu populations, giving some credence to the idea that ecological differentiation could be important.The male-killer both transmitted and functioned well following introgression to each of 24 independent isofemale inbred lines carrying Hokkaido genetic backgrounds. This was maintained even under stringent conditions of temperature. We therefore reject the hypothesis that absence of infection is due to its inability to kill males and transmit on the Hokkaido genetic background. Further trap data indicates that The absence of the infection from the Hokkaido population is not caused by failure of the male-killer to function on the Hokkaido genetic background. Selfish genetic elements that distort the sex ratio of their host are known widely in arthropods . DespiteDrosophila pseudoobscura, frequency declines at both high latitude and high altitude. This is not associated with resistance to the action of the driver (no resistance is known), and variation in rates of multiple mating is suspected as a cause [D. subobscura, X drive is present in North Africa and absent in Europe. Here, absence of X drive is associated with the less efficient function of X drive on the European genetic background [Factors causing variation in prevalence/incidence over space may be either ecological or associated with differences in host genetic constitution. For instance, in the case of X chromosome meiotic drive in  a cause . In contckground .Drosophila prosaltans, for instance, there is intra-population host genetic variation in refractoriness to male-killer action/transmission [et al. [This study pertains to the factors affecting the incidence of male-killing bacteria. These bacteria pass from a female to her progeny, and kill any males they enter. Male-killers are common in insects, but an appreciation of the factors underlying their incidence is lacking . In the smission . Thus, ismission . For ins [et al.  have demWolbachia in Drosophila bifasciata in Japan. Drosophila bifasciata feeds on sap fluxes in deciduous forests, and sampling across 10 populations within Honshu island in Japan revealed a relatively constant frequency of infection, with between 5 and 7% of females infected with the male-killer [Wolbachia still present on Honshu to this day [In this paper, we examine the factors that could cause incidence variation for the male-killing e-killer . The malthis day .D. bifasciata populations [In contrast to collections from Honshu, past surveys across Hokkaido, the North island of Japan, indicated flies in this area are not infected with the male-killer, despite the relative proximity of the sites to the infected populations in the Northern most sites in Honshu. In total, 559 flies from six locations within Hokkaido were tested, with no evidence of sex ratio distortion in any case . We can ulations . Second,D. bifasciata ecology between Hokkaido and Honshu islands. Our results indicated that the Hokkaido genetic background supported the transmission and action of the male-killer even under stringent conditions, ruling out genetic differentiation as a cause of the absence of the male-killer from Hokkaido. We did observe higher capture rates of D. bifasciata in the island of Hokkaido, and future work should therefore be focussed on the degree to which ecological differences affecting the drive of the infection dictates incidence in this species.We tested whether the absence of infection in Hokkaido was associated with an effect of host genotype on the efficiency of male-killer transmission or strength of male-killing ability. Beyond this, we examined whether trap collection data were consistent with difference in Twenty-eight female flies were collected from the field in Hokkaido. Of these, 4 failed to produce progeny. The remaining 24 all produced a normal sex ratio, consistent with continued absence of the male-killing trait in Hokkaido.Wolbachia from Honshu by introgression of the infection onto the Hokkaido genetic background. The progeny from the 24 Hokkaido females were maintained as isofemale inbred lines for three generations to capture genetic variation within them. The male-killing Wolbachia from Honshu was then placed onto each inbred background via crossing infected females from Honshu to males from each of the Hokkaido lines, with subsequent generations being maintained through further crossing to males from the appropriate Hokkaido line.We tested whether the Hokkaido genetic background supported the male-killing The sex ratio produced following introgression of the infection to the Hokkaido genetic background was female biased and penetrance of the male-killing phenotype was perfect in the first generation. A few males appeared sporadically in six of the 24 lines in one or more subsequent generations, with highest frequency in lines 15 and 25  than in the two samples from the Northern Honshu populations  and the population from Mid Honshu (Kiyosumi). Increased capture rate in Hokkaido was also reflected in an increase in the proportion of all drosophilids sampled that were bifasciata . This high efficiency was maintained even at the threshold for complete male-killing in Honshu, 23.5 C . Thus, the male-killer is proficient at being transmitted and killing males on the Hokkaido background even under relatively stressful environmental conditions. Further, we continue to maintain the infection on the Hokkaido background (we are now at generation 15) without any loss of the infection and without appearance of males. Thus, it is not tenable to argue that the hosts themselves are not genetically suitable for the function of male-killer, at least on an ecological timescale. This situation contrasts with the case of meiotic drive in the related fly D. subobscura, where absence of drive in Europe was associated with refractoriness to the action of the sex ratio distorting element.Previous study has shown that male-killing bifasciata captured at lower rate in the populations from Honshu than in Hokkaido. Thus, ecological heterogeneity is a possible source of the incidence pattern. There are three parameters in male-killer dynamics that may be environmentally influenced. First, the advantage to male-killing may not be as strong in Hokkaido populations. Second, the cost of infection to female flies may be higher in Hokkaido than in Honshu. Third, the transmission efficiency may alter, mediated via elevated temperature, or possibly by reduced overwinter temperature.In the absence of variation in the ability of the male-killer to function on the different genetic backgrounds, the question arises as to the features that cause infection to be absent from the Hokkaido population. Our study did reveal differences in trap collection rates between the populations of Hokkaido and those of Northern Honshu, with In our view, the latter factor is unlikely to be driving the observed pattern. It is notable that the survey of Ikeda revealed the infection to be present in Northern Honshu, but not in Southern Hokkaido. Given these two areas are geographically and climatically very close, temperature differences have poor explanatory power. Explanations based on temperature are also weak because this species exhibits a degree of homeostasis in temperature, moving to elevated altitudes to avoid excess temperature.bifasciata observed in Hokkaido translates into many females laying eggs in a single sap flux, male death will not greatly increase the survival of infected females over uninfected, and infection will decline in frequency. The second factor that may cause infection to decline in frequency is if costs of infection are higher in Hokkaido than in Honshu. This factor can be ecologically contingent. Ikeda demonstrated that the relative fitness of infected flies compared to uninfected flies was lower under high densities in the laboratory [This leaves us with two hypotheses to explain absence of infection in Hokkaido. The first is that there is a weaker advantage to male-killing in Hokkaido than on Honshu, such that there is insufficient drive to maintain the bacterium or permit its spread. The advantage of male-killing to the bacterium depends upon the number of females ovipositing in a single patch . If the boratory . Thus, iWolbachia from Hokkaido.Aside from these possibilities, which we consider most likely, other ecological discontinuities between Hokkaido and Honshu deserve investigation. Some symbionts, for instance, give the host protection against parasitoids , and thuWolbachia to function on different host genetic backgrounds that drives presence or absence of infection in the D. bifasciata- male-killing Wolbachia system. We have demonstrated that despite being absent from Hokkaido, Wolbachia can both be maintained and express male-killing on the Hokkaido host genetic background. We observe that the two populations show differences in trap capture rates, and argue that either ecologically contingent benefits or ecologically contingent costs of infection may explain presence and absence of infection in this species, and that future research be focussed at this level.It is not variation in the ability of D. bifasciata were collected on the campus of Hokkaido University, Sapporo , Japan, in May 2003. These were then brought into the laboratory, where they were maintained individually in vials at 21°C on a modified cornmeal-agar diet . These female were checked for the presence of the male-killing trait through scoring of the sex ratio, and maintained by sib-sib inbreeding  for three generations to make inbred isofemale lines. In total, 24 isofemale inbred lines were created (4 lines went extinct), which were maintained thenceforth by simple tossing every three weeks.Twenty eight wild female Wolbachia infected virgin females extracted from a culture derived from Honshu island, Japan . This pished in ) with liTemperature is known to affect the stability of the male-killing trait, and previous study demonstrated that an upper threshold of 23.5 C existed for stable maintenance of the infection on the Honshu genetic background . We testDrosophila communities. Drosophila communities were sampled in 4 deciduous forests in Southern Hokkaido, 2 sites in Northern Honshu, and one site in Mid Honshu, in early-mid October over a number of years between 1973 and 1984 (Figure D. bifasciata is a typical forest-canopy dweller, sampling from the forest canopy is essential for estimating its population density. These traps were cleared 7 or 10 days after setting, and the capture rate of bifasciata per trap per day calculated at each locality to represent the density of this fly in this region. All drosophilid flies were identified, and the proportion of flies caught that were bifasciata recorded.Evidence of differences in fly density can be derived from sampling the ZV helped with the design of the crossing scheme, conducted the crosses involved, and performed the analysis of these crosses. MT designed and conducted the field sampling and scored trap collections. GH conceived the project, organised collection, helped with design of the crossing scheme, and wrote the paper. All authors read and commented on drafts of the manuscript, and approved the final manuscript.


Yield of hybrids parented by the pyramided genotypes was more than 50% higher than that of a control market leader variety under both wet and dry field conditions that received 10% of the irrigation water. This demonstration of the breaking of agricultural yield barriers provides the rationale for implementing similar strategies for other agricultural organisms that are important for global food security.Natural biodiversity is an underexploited sustainable resource that can enrich the genetic basis of cultivated plants with novel alleles that improve productivity and adaptation. We evaluated the progress in breeding for increased tomato  Dramatic improvements in crop yield  can be achieved by breeding selected genetic regions from wild species into domesticated varieties Plant evolution under domestication has led to increased productivity, but at the same time it has narrowed the genetic basis of crop species . A majorSolanum pennellii and introduced (through genetic crosses) onto the genetic background of the elite inbred variety M82 (Solanum). The ILs constitute an “exotic library” where the entire wild-species genome was partitioned among 76 lines each carrying a single homozygous introgressed segment. Implementation of this resource for QTL mapping is based on the nearly isogenic nature of the lines such that any phenotypic difference between M82 and an IL, or the hybrid of M82 with an IL (ILH), is attributable to the S. pennellii genomic segments . The ILs comprised marker-defined genomic regions taken from the drought-tolerant wild species segments . Similarsegments .in silico, in a search engine that displays a range of statistical and graphical outputs that describe the components of the genetic variation  and their soluble-solids content (mainly the sugars glucose and fructose), which is measured in refractometer brix (B) units (expressed as a percentage). Therefore, agricultural yield of processing tomatoes is the total sugar output per unit area ; the industry is searching for varieties that excel in BY. Our experiments were conducted in wet and dry fields, where the BY of the control variety M82 in the dry conditions was only 50% of that produced in the wet treatment, which received 10-fold greater irrigation .In “ketchup tomatoes,” which are used to produce various concentrated products, agricultural yield is made up of the total weight of the fruits harvested per unit area  in the dry fields. IL7-5-5 did not affect B, while for BY it was dominant. IL8-3 was greatly inferior to M82 for Y , but the ILH increased Y by 45% and 25% . This result indicates a strong overdominant effect for the introgression . The homozygous IL had double the effect on B relative to the ILH; this resulted in a strong overdominant effect on BY in both environments . The reduced Y of IL8-3 was caused by a pleiotropic effect of a leaf necrosis gene that was observed in all lines that were homozygous for this introgression; the necrosis was particularly severe in the dry treatment. IL9-2-5 significantly increased Y only in the homozygous condition in the wet treatment. For B and BY, ILH9-2-5 was intermediate between M82 and the homozygous IL, showing an additive mode of inheritance. The nature of the genes that improve BY in the above introgressions is unknown, with the exception of IL9-2-5, which harbors at least two QTL that affect the components of BY  that increases sugar content of the fruit as a result of a modification of enzyme functions , Chromosome 8 (IL8-3), and Chromosome 9 (IL9-2-5) that affect the components of BY. ts of BY . One of mponents .S. pennellii segments were pooled, using marker-assisted selection, into a single M82 line designated IL789 . IL789 showed a strong interaction with the environment: In the wet treatment it produced a Y similar to that produced by M82, while in the dry fields Y was reduced by 36% as a result of the pleiotropic effect of the recessive leaf necrosis gene on IL8-3. In the heterozygous condition IL789 dramatically increased Y in both field environments, and combined with the increases in B, ILH789 improved BY by 109% in the wet field and 58% in the dry fields. As described in earlier studies . BY of BOS3155 was in a range similar to that of our experimental hybrids, indicating that the experiment was conducted in elite genetic backgrounds.Our breeding program within the ermplasm A. The BYermplasm B. This iermplasm . As a reS. pennellii pyramid as the mean difference between the nearly isogenic hybrid groups: 100% (wet) and 65% (dry). These estimates for the exotic effects on BY are very close to those obtained in the uniform M82 background, indicating additivity of the exotic and cultivated effects  interactions and the stability of BY improvement associated with the heterozygous ressions . Generalressions . In the ressions . In the We have demonstrated that an IL population derived from a wild tomato species, with no yield potential, can make a wide array of previously unexplored genetic variation rapidly available to plant breeders to improve crop productivity. The effectiveness of the introgressions in diverse genetic backgrounds indicates that alleles similar to those of the wild species are not present in the cultivated tomato gene pool. The results presented here using the tomato ILs establish a genetic infrastructure to explore the molecular basis underlying yield heterosis. With the coming sequence of the tomato genome it will be easier to isolate those factors that are responsible for the strong overdominant effects, such as those observed for IL8-3 and some additional lines that are described in the IL phenotypic database Real Time QTL . Finally2. In 2003, trials were conducted in two locations: Akko and Mevo-Hama, in the Golan Heights. In Akko, experiments were both at wide-spacing planting density and in plots of 14 plants per 4 m2 (3.5 plants/m2). In Mevo-Hama all experiments were at the wide-spacing density. The seedlings were grown in a greenhouse for 35–40 d and then transplanted in the field, at the beginning of April in Akko, and at the beginning of May in Mevo-Hama. In all seasons and locations both wet and dry trials were conducted. Both the wet and dry fields started the growing season at “field capacity,” which represents the maximum amount of water that the soil could hold. For the dry treatment only 30 m3 of water was applied per 1,000 m2 of field immediately after transplanting. In the wet treatment 320 m3 of water was applied per 1,000 m2 of field throughout the growing season according to the irrigation protocols in the area. All experiments were transplanted in a randomized block design.The results presented are from three growing seasons. In 2001 and 2002 all field trials were conducted at the Western Galilee Experimental Station in Akko, Israel, at a wide-spacing planting density of 1 plant per mS. pennellii genomic segments . Lines homozygous for each of the segments and IL789 homozygous for all three introgressions were selected and verified with RFLP markers that flanked the introgressed segments from both ends (http://www.sgn.cornell.edu/). Phenotyping of the plants for Y, B, and BY was performed according to published protocols  was half of the difference between each IL and M82, and its significance level was determined by the comparison between the IL and M82. The dominance deviation (d) is the difference between ILH and the mid-value of its parents. Its significance level was calculated by contrasting the ILH (+1) with M82 (−0.5) and the appropriate IL (−0.5). The degree of dominance for each introgression (d/[a]) was calculated by dividing the mean dominance deviation by the mean additive effect. Deviation of the observed yield component values of the pyramided genotypes (IL789 and ILH789) from the expected values based on the assumption of additivity of the effects of the individual introgressions was tested using a t test at p < 0.05. G × E interaction was tested using a two-way ANOVA.Statistical analyses were performed using the JMP V.5 software package . Mean values for the parameters measured for the tested genotypes were compared using the “Fit Y by X” function and “Compare all pairs” (Tukey-Kramer). All calculations were performed with the phenotypic values, while some of the results are presented as the percent difference from M82. The additive effect


Little is known about the history and population structure of our closest living relatives, the chimpanzees, in part because of an extremely poor fossil record. To address this, we report the largest genetic study of the chimpanzees to date, examining 310 microsatellites in 84 common chimpanzees and bonobos. We infer three common chimpanzee populations, which correspond to the previously defined labels of “western,” “central,” and “eastern,” and find little evidence of gene flow between them. There is tentative evidence for structure within western chimpanzees, but we do not detect distinct additional populations. The data also provide historical insights, demonstrating that the western chimpanzee population diverged first, and that the eastern and central populations are more closely related in time. Common chimpanzees have been traditionally classified into three populations: western, central, and eastern. While the morphological or behavioral differences are very small, genetic studies of mitochondrial DNA and the Y chromosome have supported the geography-based designations. To obtain a crisp picture of chimpanzee population structure, we gather far more data than previously available: 310 microsatellite markers genotyped in 78 common chimpanzees and six bonobos, allowing a high resolution genetic analysis of chimpanzee population structure analogous to recent studies that have elucidated human structure. We show that the traditional chimpanzee population designations—western, central, and eastern—accurately label groups of individuals that can be defined from the genetic data without any prior knowledge about where the samples were collected. The populations appear to be discontinuous, and we find little evidence for gradients of variation reflecting hybridization among chimpanzee populations. Regarding chimpanzee history, we demonstrate that central and eastern chimpanzees are more closely related to each other in time than either is to western chimpanzees. Common chimpanzees have been classified further into three populations or subspecies based on their separation by geographic barriers : western (P. troglodytes verus), central (P. t. troglodytes), and eastern (P. t. schweinfurthii) . Ea. Ea20]. The multilocus dataset also provides power to identify individuals with multiple ancestries and to assess their ancestry proportions. This cannot be done reliably using studies of single loci such as the Y chromosome or mtDNA, because individuals can in fact be descendants of multiple ancestral populations without carrying DNA from some of the populations at the locus being studied. The STRUCTURE analysis identified nine individuals as having more than 5% genetic ancestry from two clusters .P. t. vellorosus . In. In17]. llorosus . The twoWe also used STRUCTURE to validate a minimal set of markers that could be useful for classifying chimpanzees in conservation studies . The topp < 10−12) and nearly perfectly separate western, central, and eastern chimpanzees, and bonobos (p = 0.011) is also significant, and the fifth is not significant (p = 0.44).We next carried out principal components analysis (PCA). This approach has been shown to have similar power to capture population structure as STRUCTURE, but also provides a formal way of assigning statistical significance to population subdivision . When th bonobos . Only si bonobos . The foup < 10−10) for the first three eigenvectors but insignificant for the fourth (p = 0.97), indicating that this eigenvector is capturing population subdivision that is different from the traditional western/central/eastern/bonobo designations.The eigenvectors are strongly correlated to the population labels. We used nonparametric analysis  to explore whether the values of each sample along the four significant eigenvectors were significantly correlated to the four pre-existing population labels. The overall statistic is highly significant (n = 49), central chimpanzee (n = 16), eastern chimpanzee (n = 6), and bonobo (n = 6) samples . Western chimpanzees are the only population with evidence for internal substructure (p = 5.5 × 10−5). The first eigenvector obtained when western chimpanzees are analyzed by themselves strongly correlates to the fourth eigenvector in the main analysis   , indicatAlthough the fourth eigenvector seems to be detecting real structure, it does not mark out discontinuous subpopulations of western chimpanzees . The faiCould there be additional population structure  among thp < 0.05). This allowed us to assess power to detect an additional population as diverged as the eastern chimpanzees.To place an upper bound on further structure especially among the central chimpanzees, we considered the possibility that, among the 16 central chimpanzees, a subset is from a different population. We performed PCA on 10 central/6 eastern, 11 central/5 eastern, 12 central/4 eastern, 13 central/3 eastern and 14 central/2 eastern chimpanzees and assessed what fraction of 1,000 random resamplings of central and eastern chimpanzees showed evidence of structure     as an F1, with a likelihood ratio (LR) of ∼24,000,000:1. The F2/backcross test identifies the captive-born individual number 68  as an F2/backcross, with an LR of ∼37:1 . There are no other hybrids identified by either the F1 or F2/backcross test, suggesting that the other animals in Of the nine putative hybrids identified by STRUCTURE, the F1 test produced a particularly intriguing pattern in number 54, a wild-caught individual with mtDNA that has been hypothesized to be diagnostic of P. t. vellorosus origin [F1 hybrid. However, a careful examination shows that the pattern of variation at number 54 fits neither the hypothesis of a first generation hybrid or an older mixture. To demonstrate this, we simulated 100 different western-central F1 hybrids and 100 older western-central mixtures by random sampling from the population allele frequencies. Simulated older mixtures always generated an LR of >100,000:1 relative to the alternative hypothesis of an F1. Simulated F1 hybrids always gave an LR < 1:2. The LR for individual number 54 of 7:1 falls outside of either expectation. This individual fits neither model, suggesting ancestry from an as-yet undetermined population.The s origin ,10. IndiRST statistic, a microsatellite-based estimator of FST [RST assumes the stepwise mutation model (SMM), in which the number of repeats changes by one or two or more units with an equal probability of increasing or decreasing. A goodness of fit test suggests that this simple model provides a reasonable match to our data  western-central and bonobo-eastern forming clades, (2) western-eastern and bonobo-central forming clades, and (3) eastern-central and bonobo-western forming clades. If a tree provides a good description of the history of the population, then the allele frequency differences between two populations should only reflect changes since they split. For example, the difference in allele frequency between central and eastern chimpanzees should have arisen entirely since their divergence from a common ancestral population and so should be uncorrelated to the allele frequency differences between western chimpanzees and bonobos.p = 0.00025 and p = 0.0027, respectively (p = 0.37). Thus, our analysis does not find any evidence for gene flow between western and central chimpanzees since their initial split, as has previously been hypothesized [To implement this idea, we calculated the difference in frequency within clades for all alleles and then tested for a correlation across clades. When we carry out this analysis for the first and second hypothesized trees, a correlation is observed, rejecting these trees at a significance of ectively . The hypthesized . If genetMRCA) of two chromosomes is expected to be ASD/2μ, where μ is the average mutation rate per year per locus, averaged across loci. Because allele lengths change according to a random walk, the ASD between allele lengths in two chromosomes is expected to increase linearly with time and is thus expected to act like heterozygosity in sequence comparisons. By averaging ASD over pairs of chromosomes within and across populations, we can estimate the average tMRCA within and across populations.To estimate the times of genetic divergence among chimpanzees, we used the average squared distance (ASD) statistic . For mictMRCA within and across populations, we used two estimates of the microsatellite mutation rate. The first, μ = 6.57 × 10−3 per year, relies on a 7 Mya estimated average tMRCA between humans and chimpanzees and the observation that two western chimpanzees are ∼14.8 times less genetically diverged than humans and chimpanzees [−5 per year, based on estimated rates of microsatellite mutation in humans, and assuming 15 years per generation (The results confirm that genetic diversity (heterozygosity) is least for western chimpanzees and bonobos, higher for eastern chimpanzees, and highest for central chimpanzees, consistent with results obtained from a nucleotide resequencing dataset . To estimpanzees . We alsoneration .tMRCAs should be treated with caution because of uncertainty about the microsatellite mutation rate process and the calibrations used to obtain absolute dates. Nevertheless, the tMRCA estimates are consistent with previous results from smaller resequencing based datasets [tMRCAs are all similar, which appears at first to contradict the claim that the populations split at different times. However, most of the genetic divergence reflects ancestral diversity, which is shared among all the chimpanzees . MorWe have carried out the largest analysis of chimpanzee genetic variation to date, which shows that the western, central, and eastern chimpanzee subspecies designations correspond to clusters of individuals with similar allele frequencies that can be defined from the genetic data without regard to the population labels. Moreover, we find little evidence for admixture between groups in the wild. Our analysis also provides the first formal test showing that the central and eastern chimpanzee populations are more closely related to each other in time than either is to western chimpanzees.P. t. vellerosus. However, the failure to detect a distinct population cluster for these individuals could simply reflect a lack of power. Our analysis does allow us to state that even if P. t. vellorosus is a distinct population, its level of allele differentiation from either western, central, or eastern chimpanzees is not likely to exceed FST = 0.09.PCA also further suggests population structure within western chimpanzees. However, more data—more samples, genetic markers, and information about geographic origin—would be needed to understand this structure. We find no support for the proposed fourth population of common chimpanzees We finally emphasize that although we attempted to include chimpanzees from as wide a range of sites in Africa as possible, the geographic sampling of the chimpanzees that we studied was likely nonrandom. The fact that our study did not include chimpanzees from some regions—including where chimpanzees are now extinct—could create the appearance of too much discontinuity . Future The samples for this study were assembled from four sources: DNA collections at the Max Planck Institute , Anne Stone's laboratory at Arizona State University , the Corhttp://www.preventiongenetics.com). The microsatellite markers all contain tandem repeats of two, three, or four nucleotides that vary in repeat number across individuals. For example, at a single marker, different individuals might have between three and 11 contiguous repeats of a GATA sequence. The assays used for genotyping were designed for humans. However, we hypothesized that many of them would work for chimpanzees because of the ∼98.8% sequence similarity [We assembled all the DNA samples at a single site (the Broad Institute) and carried out whole-genome DNA amplification (WGA) for all samples to generate a quantity sufficient for analysis . The WGAmilarity .F1/F2 hybrid analyses. Genotypes for all markers are available in Assays were attempted for 470 microsatellites. Most came from the Marshfield Screening Set 13  and A subset of 84 of the 91 genotyped samples were chosen for further study after removing two due to a high missing data rate, one due to evidence for contamination (more than two genotypes at many loci), and four due to evidence of genetic relatedness: two accidental duplicates, and two apparent first degree relatives. For each pair of related individuals, we dropped the one with the lower success rate. The duplicate individuals allowed us to assess genotyping quality. For the two individuals studied in duplicate, 1.18% of genotyping calls differed, suggesting an error rate per genotype of ∼0.59% .We applied two complementary methods to characterize population structure in chimpanzees. First, we used the software STRUCTURE in the “admixture” mode, so that individuals were allowed to have ancestry from multiple populations. We used a model of correlated allele frequencies, a “burn-in” of 100,000 Markov Chain Monte Carlo (MCMC) iterations, and 1,000,000 follow-on MCMC iterations.We also analyzed the data using a new implementation of PCA  availablHw, the average heterozygosity within an individual's two chromosomes, and Rw, calculated as m1 and m2 are the alleles' number of repeat units at marker m within an individual, and M is the total number of markers considered. We used the average value of Hw and Rw over individuals in a population to test the hypothesis of random mating and assessed significance by a permutation test. Specifically, we generated 1,000 samples of n individuals, randomly assigning to each of them two alleles from the pool, then counted how often Hw or Rw was as small or smaller than observed. Hw was significantly lower than expected for all samples considered (at the p < 0.05 level), while Rw was significantly lower in wild-caught western chimpanzees . Thus, we analyzed 48 western chimpanzees, including 34 wild-caught and 14 captive-born individuals. We also analyzed 13 wild-caught central chimpanzees . We consmpanzees .For other analyses in which we were interested in studying only individuals identified unambiguously as being from one population, we excluded the captive-born individuals defined as putative hybrids by STRUCTURE.i repeats, is an estimator of 2Nμ, the population mutation parameter [Nμ for the 221 tetra-, 62 tri-, and 11 dinucleotides included in other analyses. Averaging this estimate over all markers of the same type and dividing by N = 10,000 [μ for each type of marker, −3, 1.91 × 10−3, and 2.17 × 10−3, respectively. These estimates are roughly similar to independent estimates [−4, 7.10 × 10−4, and 1.51 × 10−3, respectively.We examined whether our data fit the SMM in the 49 individuals that are identified as western chimpanzees by both STRUCTURE and PCA. We focused on western chimpanzees because they have the largest sample size and hence provide us with the most power to detect a departure from the SMM. Under the one-step SMM, arameter . For eac= 10,000 , we obtastimates  based onTo assess the goodness of fit of the SMM, we compared the observed distribution of SIMCOAL2 . We geneSIMCOAL2  of 10,00 the SMM .F1 hybrid of two known populations versus the alternative that it is a 50%–50% mixture . For a given autosomal marker, one can compute a log-factor under the assumption that the allele frequencies are known; these log-factors can then be summed across all markers. In practice, our population samples are small, and so allele frequency estimates are imprecise. To account for uncertainty in the allele frequencies, we used a hierarchical Bayesian model for the unknown frequencies, with a Dirichlet prior distribution for the frequencies . We verDataset S1(305 KB TXT)Click here for additional data file.Figure S1Here, we present the correlation for the 49 individuals that are clearly identified as western chimpanzees by both STRUCTURE and PCA, demonstrating that these eigenvectors are revealing the same population structure.(11 KB PDF)Click here for additional data file.Figure S2Distributions of the expected (51 KB PDF)Click here for additional data file.Protocol S1(51 KB PDF)Click here for additional data file.Table S1(22 KB PDF)Click here for additional data file.


Drosophila wing shape has emerged as an attractive system for genetic dissection of multi-dimensional traits. We utilize several experimental genetic methods to validation of the contribution of several polymorphisms in the Epidermal growth factor receptor (Egfr) gene to wing shape and size, that were previously mapped in populations of Drosophila melanogaster from North Carolina (NC) and California (CA). This re-evaluation utilized different genetic testcrosses to generate heterozygous individuals with a variety of genetic backgrounds as well as sampling of new alleles from Kenyan stocks.Quantitative differences between individuals stem from a combination of genetic and environmental factors, with the heritable variation being shaped by evolutionary forces. Egfr promoter, had replicable effects in all new experiments. However, expanded genotyping of the initial sample of inbred lines rendered the association non-significant in the CA population, while it persisted in the NC sample, suggesting population specific modification of the quantitative trait nucleotide QTN effect.Only one variant, in the Dissection of quantitative trait variation to the nucleotide level can identify sites with replicable effects as small as one percent of the segregating genetic variation. However, the testcross approach to validate QTNs is both labor intensive and time-consuming, and is probably less useful than resampling of large independent sets of outbred individuals. Elucidation of the specific genetic variants that underlie natural phenotypic variation constitutes a major challenge for evolutionary geneticists. Our understanding of evolution will remain incomplete until the relative proportions of deleterious, (nearly) neutral and adaptive factors are documented, in terms of number of loci, their individual and joint effects as well as mode of expression . SeveralD. melanogaster, QTL loci have also been dissected with quantitative complementation tests [Most common implementations of quantitative trait locus (QTL) mapping have low bias with respect to genomic coverage, but only identify allelic variation between two strains. In model organisms, these approaches allow assessment of marginal and epistatic effects, since the experiments are conducted with a large number of offspring, often in laboratory settings that reduce environmental variance. In practice, QTL are rarely resolved to individual loci or exact causal genetic variants -5, althoon tests ,10 and/oon tests ,11-20 anon tests -23.D. melanogaster can be implemented with varying degrees of control over genetic and environmental variance from wild caught individuals, laboratory reared iso-female lines, inbred strains, chromosome extraction lines and strains with introgressed chromosome regions. It is now clear that the power and resolution of association studies varies among organisms according to the extent of haplotype structure, and that different experimental approaches must be taken to verify associations in each organism. Despite the lesson from LD mapping in humans that extensive repetition, across cohorts and populations, is crucial to verify allelic contributions [Successful implication of allelic and nucleotide variation in candidate genes in the production of phenotypic variation is aided by low amounts of LD, due to substantial historical recombination, in the fly genome. LD mapping in ibutions ,25, replDrosophila wing shape has been used extensively as a model for the study of integration of developmental and quantitative genetics ,27 and fEgfr locus and shape, by sequencing ~11 kb of the locus in 210 inbred lines from two North American localities, NC and CA [Egfr with aspects of wing shape and size, either as main effects or by interaction with population or sex, were reported. A follow-up with wild caught flies confirmed one of the associations, suggesting that QTN effects responsible for less than one percent of the variation for a complex trait can be isolated [QTL mapping and quantitative complementation tests support the involvement of venation loci, including components of the EGFR/Ras pathway, in naturally occurring wing shape variation ,41. ThesC and CA ,48. Signisolated .Egfr polymorphisms to naturally occurring variation for wing shape and size. Three schemes were employed, two involving crosses among a subset of the NC lines , and a third involving test crosses between an independent set of Kenyan second chromosomes and the Samarkand wild-type and EgfrE1 and blistered1 mutant alleles  descriptors of shape, and although these are modified subtly by inclusion of more wing data  overall eference ). Furthesize and Egfr polymorphisms, recrossing of inbred lines used earlier and testcrosses of additional African chromosomes was carried out. Neither of the two variants affecting size of the wing (C31656T and T40722C) in the initial study gave a significant association in any of the three new datasets (Table p = 0.000002) that also exhibited a possible three way interaction of Population, Sex and Genotype (p = 0.001). As the three-way interaction was primarily caused by larger difference in the CA than the NC sample [Egfr with wing size was likely a false positive even though it was significant after adjustment for the number of multiple comparisons experiment-wide.In order to re-evaluate our previously published associations between wing C sample , the lacEgfr variants to aspects of wing shape. Only one polymorphism T30200C, was significant and had consistent effects in all of these experiments. This variant resides in the second alternate promoter in a putative GAGA factor binding site, and contributes to the first principal component of the central region of the wing  and also each of the two site haplotypes had given highly significant association in the original panel of inbred lines. Due to smaller sample size in our recrossing datasets, testing of this pattern could only be conducted with the BC dataset, but the previous epistatic interactions were not confirmed (data not shown). In summary, only one of the Egfr polymorphisms previously implicated to impact wing shape was corroborated by the new data.Neither of two other previously reported putative associations , the sexp = 0.00002) but only marginal association in the west coast sample (p = 0.04) (see p = 0.002). More dramatically, the addition of 30 more alleles to the CA lines (N = 76) rendered the originally marginal association non-significant (p = 0.9) (p > 0.05).Previously, due to incomplete genotyping around exon 2, the contribution of T30200C to the central region of the wing was only evaluated with 79 NC and 43 CA lines . Analyse.04) see . In ordep = 0.9) . Inspectp = 0.9) , as the bs1 carrying chromosomes).Estimates of the genotypic effects of T30200C on wing shape are comparable across all of the datasets. There was a slight reduction in observed contribution after the extra genotyping , and theEgfr locus is plotted for the three experiments in Figure p = 0.05 and p = 0.0001 as before [Egfr.In order to compare the gene-wide patterns of association for each design, the association statistic for the Genotype effect of each site along the s before . The anap = 0.05. The observed jaggedness of the association profiles likely reflects stochastic fluctuations in the p-values in experiments with relatively small sample size. One interpretation of the data is that the inbred and backcross designs provide better dampening of this stochastic fluctuation then do studies with round robin crossed inbred lines.The first general result is that the small sample of Kenyan introgressions provides more highly significant sites than the total NC sample (with the exception of T30200C there are no significant associations in common between these two populations). Similarly the RR design yielded more significant test statistics (three sites in the first exon) then the BC or inbred panels and had 55 sites exceeding the test-wise significance threshold of The second result is that, in both the RR and BC experiments, the shape of the association profile tracks quite closely with that of the corresponding profile for the set of nearly isogenic lines used to set up the testcrosses. This was not anticipated, since NC025 and NC144 lines have very different wing shapes and each contribute 25% of the genetic variation in the BC, while the RR combines the genetic variation of the 71 inbred lines in equal proportions. Evidently genetic correlations between the different testcrosses are sufficient to produce similar association profiles, whether or not these accurately report QTN effects.Egfr affect the cross-vein placement we performed a combined Mixed model ANOVA on the three NC datasets . Eleven independent polymorphisms summarized in Table p < 0.0001, including site T30200C. Most of these sites are not significant in the CA and Kenyan datasets, but the direction of the genotypic effects generally correspond with the NC panels . Only one of these new candidate variants, C6085G in the less conserved of the two alternate first N-terminal exon, alters the protein, while the remaining are non-coding or silent. Interestingly, one of these silent polymorphisms is C40620T, which also associates with cryptic variation for eye-roughness in inbred lines and wild flies [Egfr variants are tested against other principal component measures of wing shape, similar number of sites emerge at the level of p < 0.0001 (data not shown) suggesting the caveat that this approach may be inherently noisy.Finally, in order to test whether other sites in ld flies . Note hoEgfr and wing shape and size in D. melanogaster in 210 inbred lines from two North American populations [Egfr variants or linked polymorphisms as causal variants. In this study we aimed to re-evaluate their involvement through further genetic analysis by generating heterozygous lines derived from crosses of a subset of the original lines and by test crosses with a small sample of African chromosomes. Only one of the retested variants was significant in all datasets and gave consistent effects: the T30200C polymorphism that affects a principal component capturing variation in relative distance between the two cross-veins. However, even the estimated absolute magnitude of this effect is dependent on the survey population and crossing scheme. These results highlight the difficulties in validating weak quantitative effects through experimental genetic approaches and suggest that resampling of outbred populations may be the more conclusive approach to dissection of QTL to the nucleotide level.Previously, fine mapping of the association between polymorphisms in the candidate locus ulations  implicatEgfr do not contribute to shape of the central region of the wing. This seems unlikely, since significant association was also observed in a large sample of outbred NC flies [There are at least three possible explanations for the observed restriction of statistical support for the association of T30200C with wing shape to just two of the three populations sampled. The first is that the observed associations in NC and Kenyan samples are false positives, namely that T30200C or linked variants in NC flies  and the Egfr polymorphisms to wing shape in NC. One is that the effect of T30200C is masked by genetic variation that is unique to the CA population. Another possibility is that T30200C is not the real causative variant, but exhibits high LD with the causative site in the NC and Kenyan populations but weak LD in the CA population. Since LD in the Egfr decays to background levels over several hundred bases and no differences were observed between NC and CA in their patterns of LD or allele frequencies, while both North American populations diverge considerably from the Kenyan sample [p = 0.88), but it does lie adjacent to a 23 kb intron that has not been sequenced in the population sample and could conceivably harbor the true causative variant. However, we favor the hypothesis that one or more modifier loci that differentiate the two North American populations mask the expression of variation due to the Egfr in the CA sample.Two alternative explanations are consistent with the statistical significance being indicative of a true contribution of n sample , this laEgfr promoter, is supported by genetic interaction between the two loci [Egfr and cross vein placement is in accord with developmental genetic evidence. Specifically, flies heterozygous for different Egfr alleles lack the majority of the L4 vein and the entire proximal cross-vein [Two developmental genetic arguments also lend support to the hypothesis that the T30200C variant is the causal site. First, our prediction that this site affects a GAGA factor binding element in the two loci ,54. Secooss-vein ,55,56. RD. melanogaster are now being dissected with QTL mapping, quantitative complementation tests and by testing specific allelic variants by LD mapping. While several studies have found significant association between markers in candidate gene regions and continuous phenotypes [achaete-scute locus influence bristle number, and this inference was corroborated in a second sample [Delta polymorphisms [Egfr polymorphisms affect cryptic variation in eye roughness in inbred lines, and then confirmed the finding in an independent sample of wild caught flies [hairy on bristle number that was previously observed [scabrous in variation for bristle number, these two studies differed in which markers were typed and by criteria for evaluation of significance  are forthcoming since the different approaches only produce broadly comparable results.The round robin and backcross approaches were designed to evaluate the degree to which effects observed in inbred lines are also seen in mixed genetic backgrounds. If the effects of the SNP are additive and there is no epistasis, then they should be just as strong in the testcrosses as in the nearly isogenic lines, with the caveat that there are three genotypes at each SNP to compare instead of just two. The BC design differs in two distinct ways from the RR design, namely the reduced genetic variation  and the capacity to detect epistatic effects. This latter could occur by interaction between the QTN and other loci, either due to de-canalization as these other loci perturb the phenotype away from the population mean, or simply because QTN effects may generally be so modified by the background that they are only observed in certain backgrounds. The similarity of the estimated genotypic mean differences over the two BC backgrounds and the close tracking of means in the KI experiment Figure , suggestEgfr may affect cross-vein placement, a considerably larger sample than explored here would be required to validate these sites. The testcross results strongly suggest that we can eliminate highly significant results from the first experiment as false positives, but can not conclusively resolve the question of whether the Egfr QTL resolves to a single or several QTN.While the ten new highly significant sites in the combined model may be false negatives in the initial lines, more data would be required to confirm that they are true positives. These results indicate that recrossing and deeper population sampling has at best low power to detect novel candidate sites with subtle effects on the phenotype. Consequently, the testcrosses do not obviously outperform the inbred line analysis or bring us any closer to resolving true positive QTN from false positives. Even with a relatively large experiment such as this, the amount of labor and time spent on setting up several hundred crosses and phenotyping several thousand wings does not overcome sampling biases. Even if our analyses suggest that other sites in Egfr contribution to shape variation in D. melanogaster wings reported in Palsson and Gibson [Egfr variants failed to validate in testcrosses is that stochastic factors have a substantial impact on analysis of the genetic basis of continuous phenotypes in studies involving fewer than 200 inbred lines. Apparent conditional polymorphisms may be especially sensitive to these effects of chance, and all unreplicated association studies in Drosophila should be considered with this caveat in mind. We suggest that measurement of a very large number of offspring is essential for replication and validation in association studies, and that these are better sampled in outbred wild individuals than in laboratory lines. The declining cost of genotyping will facilitate this transition to large scale mapping of quantitative traits to single nucleotides in ecological settings.The d Gibson , and repd Gibson , represeEgfr on wing shape , and a blistered allele (bs1) were substituted into Sam. The wild-type chromosomes were tested over these two mutations and the wild-type Samarkand second chromosomes in three replicate crosses arranged in random blocks. All crosses involved three males crossed to three females, and where conducted in two (RR and BC) or three replicates (KI).Three separate experiments were conducted to re-evaluate the contribution of e Figure . Two invnd or 4th day depending on visual assessment of egg density. The right wing of eight to ten randomly selected individuals per sex  from each vial was scored. In the Kenyan introgression experiment the visible marker Cy on the balancer chromosome distinguished genotypes of lines inviable as 2nd chromosome homozygotes. Handling of specimens and data processing was identical to previous experiments [Flies were reared at 25°C in standard cornmeal medium with a constant light/dark cycle. Density was controlled by placing two virgin females and two males in a vial and discarding parents on the 2eriments . In shoreriments ). The niShape variation was summarized with the TPSrelw software version 1.39 (freeware available ) by calcEgfr haplotypes are highly similar, as 167 out the 232 common Egfr sites genotyped in both lines differ, with several recombination events evident. F1 lines that were missing a genotype of one parent where omitted from the analysis for that particular genotype. In the Kenyan sample, only the variant T40722C was not tested, as it was only available in one Kenyan line. The Egfr alleles were not sequenced in the three tester chromosomes, leading to tests on haploid data.Genotypes used for the association tests were derived from our earlier sequence data . The BC The T30200C polymorphism in the non-coding region upstream of alternative exon one  was re-gAll statistical analysis used SAS version 8.2 . The estimation of line effects and extraction of line means was implemented with the LSMEANS option in Proc GLM. The model for the RR dataset was:μ + Line + Rep(Line) + εY = bs), Sex or Line.where Line represents each of the F1 lines generated by the round-robin crosses, and Rep the replicate vial. For the Back-cross and the Kenyan introgression, a more complicated model was used, accounting for the effects of Cross  + S × R(C × L) + εY = In both models terms including Line and Rep are considered random. We also performed the analysis without Rep as a term, with the same results.The main aim of these experiments was to re-evaluate the six sites which gave significant signals for wing size and shape in . The RR μ + Gtyp + Rep(Gtyp) + εY = Gtyp is the fixed effect of Genotype, and Rep is a random term, again the replicate vials. For the back-cross and the Kenyan test cross, the model accounted for the contribution of sex and cross:μ + Gtyp + Sex + Cross + G × S + G × C + G × S × C + Line(G × C) + εY = The mean effects of polymorphisms were estimated by the LSMEANS option. Reduced models, by crosses, and extended, by including replicates were also studied and were in accord.Egfr on the C1 we utilized a related model, substituting the Cross term with a fixed experiment (Exp) term to demarcate the NC, BC and RR datasets, and restricting the analysis to females as the RR panel had no males. The sire and dam are random effects nested within the fixed effects:In order to gauge the effects of additional sites in μ + Gtyp + Exp + G × E + dam × sire(G × C) + Rep(dam × sire × G × C) + εY = Sites with probability of genotype term below 0.0001, where then investigated for consistency in genotypic effects and their significance in the CA and KI dataset.AP, GG and JD designed experiments. JD crossed and scored RR and BC experiments, AP crossed and scored KI, Inbreds and parts of RR dataset. AP conducted statistical analysis. ID regenotyped the T30200C variant. AP, ID and GG wrote the manuscript and all authors approved the final version.Egfr promoter and the first principle component for the shape of the central region of the wing.ANOVA tables for site T30200C and wing shape. The file shows the results of Analysis of Variance for the T30200C variant in the Click here for fileGenotypic effects of T30200C on the first PC of the central region of the wing. This table illustrates the genotypic effects (and standard errors) for the T30200C association to wing shape of the central region.Click here for file


What makes some people neurotic or schizophrenic or right-handed or fearless? The challenge in answering this is to map from genotype to anatomical and physiological phenotypes and beyond to behavior and cognition. What makes some people neurotic or schizophrenic or right-handed or fearless? Are these behavioural differences caused by literal differences in how individuals' brains are wired? If so, what causes those differences? This age-old question of nature versus nurture can be recast in more realistic terms based on our modern understanding of genetics, development, and neuroscience. The challenge in this area is to understand how genotype is mapped to phenotype, not just in terms of the average effects of single genes across populations but also in terms of their combined effects in shaping the phenotypes of individuals.“Things are the way they are because they got that way.”—Gerald WeinbergThere is compelling evidence that many psychiatric disorders have their origins in disturbed neurodevelopment, resulting in altered connectivity ,2. SimilThe establishment of the circuitry of the brain follows an intricate developmental programme involving cell fate specification, cell migration, axon pathfinding, target selection, and synaptogenesis . The lasThese effects can be directly observed in model organisms. In Drosophila, the projection of motor axons to the embryonic body wall muscles has been very well characterised. In this system, many genes show no effect when removed on their own but do show an effect when ectopically expressed , a prediSimilar effects are seen for behavioural phenotypes, including the well-known large effects of genetic background in mice. Greenspan and colleagues discovered complex and shifting epistatic interactions between 16 genes for a behavioural trait in flies . These fHow this kind of epistasis observed at the biological level in model organisms relates to statistical epistasis in human populations is an open and critical question . The impResearchers in psychiatric genetics are beginning to come to grips with the issue of epistasis. This will be especially important as data from large-scale whole-genome association studies emerge in the near future. Some of the methods of these studies look for epistasis between pairs of candidate genes, identified through suggestive single-gene associations with disease  or by inDrosophila embryo [Another level of complexity arises in mapping from genotype to phenotype, even in the hypothetical case where whole-genome sequence is available and all single-gene and epistatic effects are known. Monozygotic twins in humans, and genetically identical organisms in other species, show considerable phenotypic variability. This phenotypic variability can be continuous or dichotomous and is observed for behavioural traits and psychiatric disorders  and alsoa embryo .Epigenetic differences have been proposed as a mechanism to explain this intrinsic phenotypic variability . Random Waddington's “epigenetic landscape”  providesWaddington referred to the buffering of developmental systems to produce a “wild-type” phenotype in the face of various mutations or environmental insults as “canalization” . Increasing mutational load should reduce the capacity for such buffering, leading to the prediction that individuals with greater developmental “noise” should be more susceptible to disease. Such noise can be indirectly measured by examining markers of “fluctuating asymmetry”, including fingerprint asymmetry, for example, which has indeed been found to be higher in individuals with schizophrenia .The challenge for the fields of neurodevelopmental and psychiatric genetics is to develop methods to take these factors into account in attempting to map from genotype to anatomical and physiological phenotypes and beyond to behaviour and cognition. Modelling this complexity may require both new mathematical methods and more detailed empirical data derived from studies of model organisms . Whichev


Condition-dependence is a ubiquitous feature of animal life histories and has important implications for both natural and sexual selection. Mate choice, for instance, is typically based on condition-dependent signals. Theory predicts that one reason why condition-dependent signals may be special is that they allow females to scan for genes that confer high parasite resistance. Such explanations require a genetic link between immunocompetence and body condition, but existing evidence is limited to phenotypic associations. It remains unknown, therefore, whether females selecting males with good body condition simply obtain a healthy mate, or if they acquire genes for their offspring that confer high immunocompetence.Taeniopygia guttata. We show that there is significant positive additive genetic covariance between an index of body condition and an index of cell-mediated immune response. In this case, genetic variance in the index of immune response explained 56% of the additive genetic variance in the index of body condition.Here we use a cross-foster experimental design to partition the phenotypic covariance in indices of body condition and immunocompetence into genetic, maternal and environmental effects in a passerine bird, the zebra finch Our results suggest that, in the context of sexual selection, females that assess males on the basis of condition-dependent signals may gain genes that confer high immunocompetence for their offspring. More generally, a genetic correlation between indices of body condition and imuunocompetence supports the hypothesis that parasite resistance may be an important target of natural selection. Additional work is now required to test whether genetic covariance exists among other aspects of both condition and immunocompetence. Body condition is central to animal life histories because the expression of many traits critical to survival and reproductive success is condition-dependent ,2. CondiA key requirement of the CMIH hypothesis, and other related life history hypotheses -19, is tTaeniopygia guttata. Zebra finches provide an ideal opportunity to determine if this critical genetic association exists for two reasons. First, this species is a model system for the study of sexual selection, in which female choice is based on a number of condition-dependent male signals that include song rate and bill colour [The overall aim of this study was to test directly for genetic covariance between indices of body condition and immunocompetence in a small passerine bird, the zebra finch l colour -40. Secol colour . We therl colour  to estimIijk) testing for a genotype-environmental interaction for Equation (1) was not significant for our indices of either immune response  or body condition . This showed that chicks from the two sampling sites did not respond differently to nest environments from the other population. Similarly, there were no significant differences between the sites for the phenotypic means of our indices of either immunocompetence  or body condition , or the breeding values of broods for either trait  between our indices of immune response level and body condition was 0.75 ± + 0.41 Pi = average effect of the ith cross-fostered block of nests, Mij = direct effect of the jth (genetic) mother within the ith block (j = 1 or 2), Nik = kth (unrelated) nurse within the ith block (k = 1 or 2), Iijk = M × N interaction within the ith block, and eijkl = residual error for the ith offspring of the jth mother raised by the kth nurse within the lth block of nests.where, Iijk term of Eq. 1 tested for the presence of a genotype-environment interaction in this experiment as nest of origin also represented genetic population of origin and nest of rearing also represented the population of rearing. Significance of nest of origin (Mij) and nest of rearing (Nik) was tested using the interaction term (Iijk) as the error, type III sums of squares for unbalanced designs. In all genetic models genetic relationships were inferred on the basis of the male and female providing care at the nest in question, as extra-pair paternity in zebra finch colonies is low (2.4% of chicks) [The  chicks) .When using individuals from two different populations there is also the risk that such populations could differ with respect to the parameters under study. Specifically, if the populations differed with respect to both traits then pooling individuals from the two populations might generate spurious covariance between the traits. It is important to note here that the populations need to differ for both traits and not just one of them. This is because, if populations only differ with respect to in one variable, this would just increase variation along a single axis. To assess these possibilities we therefore tested for differences between the source populations in both the phenotypic means and breeding values of our indices of both immune response and body condition.We used two related methods to test for a genetic correlation between our indices of immunocompetence and body condition. To facilitate comparisons with other studies, we first used the standard method for analysing cross-foster experiments, which is based on using offspring values alone . We usedX  that were estimated by the various methods listed in Table 9. The estimation of the direct-maternal additive genetic covariance which is isolated by observational component 9 has been the source of some confusion in the literature. Rutledge et al [AOAM could be estimated using the interaction term in (1), which more recently was also advocated as an appropriate way of estimation in Lynch and Walsh (1998). However, this method of estimation was subsequently shown to be incorrect [V matrix are set to zero, see below). By using the estimation method of component 9 employed here, this potential problem is likely to be exacerbated as the estimate of component 9 is a linear combination of the mean squares used in other observational components (5 and 6). Nevertheless, component 9 as estimated here has an established interpretation, and facilitated the isolation of the important σAOAM causal component.The observation vector ge et al  first prncorrect ,56. Riskncorrect  outlinedncorrect ,57, whicncorrect  approachncorrect ,55 or heV, with off-diagonal elements all zero. To obtain variances for each observational component [Variances of the observational components were used as the diagonal elements of the square matrix omponent  for compVAR(σ2) = (VAR(A) VAR(B) + COV2) / (N)     (2)A and B represented the two kinds of individuals whose covariance is being estimated and N is the number of bivariate observations. The variance of components 5–10 were estimated as weighted sums of the variances of the appropriate mean squares, where the variance of a mean square is given by  = (2MS2) / (N + 2)     (3)MS represents the mean square of the term of interest and N is the number of blocks. The causal components of variance were estimated as elements of the vector:in which b = (X'V-1X)-1X'V-1y     (4)with covariance matrix:S = (X'V-1X)-1.     (5)S was used to estimate the standard error of b. Phenotypic variance was estimated as the sum of the elements of b and its corresponding standard error approximated by the square root of the summed diagonal elements of S.where the square root of the corresponding diagonal element of V) were determined by calculating separate variances of cross-covariances for (i) our index of body condition in parents and our index of immune response in offspring, and (ii) our index of immune response in parents and our index of body condition in offspring. The mean was then taken of these two variances of cross-covariances. The additive genetic correlation (rA) and an estimate of its standard deviation were calculated using equations 19.2 and 19.4 in Falconer [Estimation of the genetic correlation between our indices of body condition and cell-mediated immune response level required all observational components to be estimated as cross-covariances . Cross-cFalconer , respectFalconer .DJG helped to design the project, collected animals from the wild, conducted all crosses and measurements, performed statistical analyses, and helped to write the paper. MWB helped with the statistical analyses and the writing of the paper. IPFO helped to design the project, collect animals, and write the paper. All authors read and commented on drafts of the manuscript, and approved the final manuscript.


There has recently been great interest in applying theoretical quantitative genetic models to empirical studies of evolution in wild populations. However, while classical models assume environmental constancy, most natural populations exist in variable environments. Here, we applied a novel analytical technique to a long-term study of birthweight in wild sheep and examined, for the first time, how variation in environmental quality simultaneously influences the strength of natural selection and the genetic basis of trait variability. In addition to demonstrating that selection and genetic variance vary dramatically across environments, our results show that environmental heterogeneity induces a negative correlation between these two parameters. Harsh environmental conditions were associated with strong selection for increased birthweight but low genetic variance, and vice versa. Consequently, the potential for microevolution in this population is constrained by either a lack of heritable variation (in poor environments) or by a reduced strength of selection (in good environments). More generally, environmental dependence of this nature may act to limit rates of evolution, maintain genetic variance, and favour phenotypic stasis in many natural systems. Assumptions of environmental constancy are likely to be violated in natural systems, and failure to acknowledge this may generate highly misleading expectations for phenotypic microevolution. An analysis of birth weight in Soay sheep reveals that environmental heterogeneity can constrain evolution and suggests that rates of evolutionary change may be lower in natural populations than expected from theory. Quantitative genetic models allow an evolutionary trajectory to be predicted from the strength of selection and the amount of genetic variance, usually expressed as the heritability,h2 . Howeverh2 ,4, and h2 ,6. Alth(Ovis aries) on the Scottish island of Hirta, St. Kilda [Here we examine the simultaneous effects of environmental variation on selection and heritability, using data from a long-term study of Soay sheep. Kilda . This sy. Kilda ,9. By s. Kilda . While sHere, we employ the analytical technique of “random regression” to model genetic variance as a function of a continuously varying environment. This not only allows a more realistic model of environmental variation, but can also provide statistical benefits . In partBWT) and its association with juvenile mortality in the Soay sheep population. Of the total mortality of sheep, 24% occurs during the neonatal period (from birth in April or May until October 1 of the same year), making it a critical episode for viability selection [We focused on the trait of lamb birthweight  is relatively low [Birthweight is also heritable, as a consequence of strong maternal genetic effects, even though additive genetic variance , thely low .p = 0.15,n = 19). We therefore tested the hypothesis that environmental heterogeneity limits phenotypic evolution through effects on heritability or selection, or both simultaneously. To do this, we first examined the impact of environmental variation on the heritable basis of birthweight by modelling levels of genetic variance across a variable environment. Secondly, we tested for systematic variation between years of differing environmental conditions in the strength of selection acting over the neonatal period.Despite both positive directional selection and heritable variation ,18, annWe estimated heritable variation in Soay sheep birthweight using an animal model approach , which a(E) based on the level of neonatal mortality in each year  is used, then an individual's maternal genetic effect cannot change withE and, consequently, variance in these effects  is also constant across environments. In contrast, fitting higher-order polynomial functions allows systematic variation in the amount of maternal genetic variance across environments to be explicitly tested for.To model genetic variation for birthweight, we defined a measure of environmental qualityyear see. An enviVM) showed a general increase withE (VA = 0.020 (0.009),VC = 0.015 (0.008), andVR = 0.116 (0.008), respectively. As a consequence of increasingVM, the evolutionary potential of birthweight, as indicated by the total heritabilityh2T . Analysis with 1987 data excluded confirmed a strong trend of increasingVM withE, as did simpler models where we used first- or second-order functions of environmental quality , also ieffects ,23. Givs years , it is aE changes. Nevertheless, maternal genotype-by-environment interaction is shown by the environmental dependence ofVM.Maternal genetic covariances between environments were also estimated under Model IV, and were found to be uniformly positive . RescalisBWT), as well as with environmental qualityE  are useful for comparison with other studies, and ranged from +0.038 (± 0.019) to a maximum of +0.529 (± 0.064). This confirms that, in some years, directional selection was considerably stronger than the median value of 0.16 reported in the literature [Our finding of environmental variation in the heritable variance for birthweight was complemented by similar systematic variation in strength of selection. The relationship between phenotype and fitness  was investigated using generalised linear mixed modelling and standard regression-based methods , and conalityE . Howeverr = −0.919, df = 17,p < 0.001), confirming that positive directional selection on birthweight is weaker in good environments , suggesting a slight upward bias in the overall selection estimate through environmentally induced positive covariance between phenotype and fitness [Annual selection differentials were also strongly negatively correlated with environmental quality (Pearson's correlationonments . Poolingfitness .h2T), obtained under Model IV, were strongly negatively correlated with annual selection differentialsS .Thus, maternal genetic variance for birthweight increases with environmental quality, while the strength of selection decreases. This shared dependence on environmental quality results in a negative correlation between the strength of selection on birthweight and the amount of heritable variation. Accordingly, annual estimates of the total heritability ((R) to selection through neonatal mortality, determined using the breeder's equation [h2T andS, were small but showed a 10-fold range in magnitude (from 0.004 to 0.046 kg per generation). The weighted geometric mean response . Predicted responses should be interpreted with some caution since many assumptions of the breeder's equation will likely be violated in this case. For example, these predictions reflect selection through neonatal mortality only, while birthweight may co-vary with multiple fitness components [Evolution of increased birthweight in response to differential neonatal mortality is therefore limited by either a lack of heritable variation in poor environments or by a reduced strength of selection in good environments. Annual phenotypic responsesquation  as the ponse see was 0.01ponents  and withponents ,28. Nevponents ,29.Our findings thus highlight the differences between populations in natural environments and those in the controlled, constant environments for which quantitative genetic theory has been developed. While suitable long-term data will not always be available, increased efforts should be made, wherever possible, to fully consider the implications of environmental heterogeneity for both selection and the genetic variation on which it acts. In particular, if favourable environmental conditions are generally associated with low levels of selection but with high levels of heritability, and vice versa, then rates of evolutionary change may be much lower than those expected from values averaged across environments, providing an explanation for the frequently-observed stasis .Soay sheep were introduced to the Scottish archipelago of St. Kilda in the Bronze Age, and to the main island of Hirta  in 1932 shortly after the human evacuation of St. Kilda. Data relating to birth, death, reproduction, and phenotype have been collected for individually tagged animals resident in the Village Bay area since 1985 . The pedj), as well as additive (ai) and maternal genetic (mj) effects on birthweight. This random-effects structure results in the total phenotypic variance (VP) being split into four components: additive genetic variance (VA), maternal genetic variance (VM), maternal permanent environment variance (VC), and the residual (or temporary environment) variance (VR). Importantly, rather than using a conventional animal model, we fitted the maternal genetic effect mj as a polynomial function of environmental qualityE using random regression. Here, environmental qualityE is defined as the proportion of lambs surviving until October 1 in the year of birth . Thus, the birthweight of any animali having motherj is given as:Quantitative genetic parameters were estimated using animal models to partition variance into genetic and environmental components. Animal models are able to accommodate unbalanced datasets and complex pedigrees typical of natural populations , and alsBWT is birthweight, ei is a residual error term (having mean zero and varianceVR), andf is the random-regression function of maternal genetic value on orthoganol (Legendre) polynomials ofE with orderx. The model structure was fitted using different orders of the polynomial function (0 ≤x ≤ 3) using restricted maximum likelihood implemented in ASReml . These alternative formulations of the model were then compared statistically using likelihood ratio tests. Model convergence was not achieved forx > 3 (unpublished data). As in a conventional animal model, an implicit assumption here is thatVA andVR are constant across environments. Univariate animal models in data subsets corresponding to good and bad environments (based on upper and lower 50 percentiles ofE) provided support for this assumption, with no significant differences in estimated additive or residual variances.WhereQ) was used to determine the maternal genetic variance–covariance matrix (M) as:The estimated variance–covariance matrix of random-regression parameters for the maternal genetic effect . Approximate standard errors for each element ofM were determined following Fischer et al. [h2T = (VA +VM/2)/VP . Note t(W) and was defined as 0 for animals that died before October 1 in the year of birth and as 1 for those that survived. Note that since offspring share common mothers, individual birthweights cannot be considered as independent data points. To account for this non-independence, we used a linear mixed model , with maternal identity fitted as a random effect such that:Neonatal survival was used as the fitness componentE is environmental quality;sBWT is birthweight standardized to mean 0 and variance 1,b0 tob3 are constants, andMOTHER is the maternal identity. AsE is determined from the average neonatal survival in each year, a statistical association between fitness and environment is inevitable . However, it is the effect ofE on selection, assessed fromb3, the interaction betweensBWT andE, that is of primary interest. Selection differentials(S) and standardized selection gradients (β) [BWT. Annual phenotypic responses(R) to selection were determined as the product ofh2T andS, and the weighted geometric mean response was calculated. Annual responses were weighted by surviving cohort size on October 1 .wherents (β)  were det